PROJECT SUMMARY The central hypothesis for the Center for Adolescent Rhythms, Reward, and Sleep (CARRS) is that adolescent development acts on underlying sleep and circadian traits to modify homeostatic sleep drive, circadian phase, and circadian alignment, which in turn impact cortico-limbic functions critical to substance use risk (e.g., reward and cognitive control). We further hypothesize that specific manipulations of sleep and circadian rhythms during adolescence will affect reward responsivity and cognitive control in either positive or negative directions. These manipulations will provide experimental support our model, and proof of concept for novel clinical interventions to reduce the risk of substance use and abuse. Core C: Data Management and Statistics will support the 5 projects in CARRS by managing data (e.g., developing protocols, forms, and databases and assuring data quality and security) and performing statistical analyses (e.g., preliminary, primary, secondary, and exploratory analyses). In this way, Core C will guarantee high-quality, transparent, and consistent standards for data management and statistical analyses across projects and will maximize rigor and reproducibility within CARRS. Core C will also develop and adapt analytic methods that take full advantage of the translational and high- dimensional data captured across the 5 projects within CARRS. Areas of focus will include: use of supervised learning (e.g., random forests) for prediction with high-dimensional data within humans and, separately, rodents; methods for integrating findings across projects and species (based on data from Projects 1-5), and quantitative ?multi-omic? methods for integrating RNA-seq and mass spectrometry data. Finally, we will educate researchers within CARRS and in the research community on existing and cutting-edge statistical methods relevant for our research. Topics will include rigor and reproducibility, analysis of sleep and circadian data, analysis of RNA sequencing and proteomic data, and the innovative statistical methods developed and applied within Core C.